当前位置:网站首页>GNN的第一个简单案例:Cora分类
GNN的第一个简单案例:Cora分类
2022-07-06 09:16:00 【想成为风筝】
GNN–Cora分类
Cora数据集是GNN中一个经典的数据集,将2708篇论文分为七类:1)基于案例、2)遗传算法、3)神经网络、4)概率方法、5)、强化学习、6)规则学习、7)理论。每一篇论文看作是一个节点,每个节点有1433个特征。
import os
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch_geometric.datasets import Planetoid
import torch_geometric.nn as pyg_nn
#load Cora dataset
def get_data(root_dir='D:\Python\python_dataset\GNN_Dataset\Cora',data_name='Cora'):
Cora_dataset = Planetoid(root=root_dir,name=data_name)
print(Cora_dataset)
return Cora_dataset
Cora_dataset = get_data()
print(Cora_dataset.num_classes,Cora_dataset.num_node_features,Cora_dataset.num_edge_features)
print(Cora_dataset.data)
Cora()
7 1433 0
Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708])
代码中给出GCN、GAT、SGConv、ChebConv、SAGEConv的简单实现
import os
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch_geometric.datasets import Planetoid
import torch_geometric.nn as pyg_nn
#load Cora dataset
def get_data(root_dir='D:\Python\python_dataset\GNN_Dataset\Cora',data_name='Cora'):
Cora_dataset = Planetoid(root=root_dir,name=data_name)
print(Cora_dataset)
return Cora_dataset
#create the Graph cnn model
""" 2-GATConv """
# class GATConv(nn.Module):
# def __init__(self,in_c,hid_c,out_c):
# super(GATConv,self).__init__()
# self.GATConv1 = pyg_nn.GATConv(in_channels=in_c,out_channels=hid_c)
# self.GATConv2 = pyg_nn.GATConv(in_channels=hid_c, out_channels=hid_c)
#
# def forward(self,data):
# x = data.x
# edge_index = data.edge_index
# hid = self.GATConv1(x=x,edge_index=edge_index)
# hid = F.relu(hid)
#
# out = self.GATConv2(hid,edge_index=edge_index)
# out = F.log_softmax(out,dim=1)
#
# return out
""" 2-SAGE 0.788 """
# class SAGEConv(nn.Module):
# def __init__(self,in_c,hid_c,out_c):
# super(SAGEConv,self).__init__()
# self.SAGEConv1 = pyg_nn.SAGEConv(in_channels=in_c,out_channels=hid_c)
# self.SAGEConv2 = pyg_nn.SAGEConv(in_channels=hid_c, out_channels=hid_c)
#
# def forward(self,data):
# x = data.x
# edge_index = data.edge_index
# hid = self.SAGEConv1(x=x,edge_index=edge_index)
# hid = F.relu(hid)
#
# out = self.SAGEConv2(hid,edge_index=edge_index)
# out = F.log_softmax(out,dim=1)
#
# return out
""" 2-SGConv 0.79 """
class SGConv(nn.Module):
def __init__(self,in_c,hid_c,out_c):
super(SGConv,self).__init__()
self.SGConv1 = pyg_nn.SGConv(in_channels=in_c,out_channels=hid_c)
self.SGConv2 = pyg_nn.SGConv(in_channels=hid_c, out_channels=hid_c)
def forward(self,data):
x = data.x
edge_index = data.edge_index
hid = self.SGConv1(x=x,edge_index=edge_index)
hid = F.relu(hid)
out = self.SGConv2(hid,edge_index=edge_index)
out = F.log_softmax(out,dim=1)
return out
""" 2-ChebConv """
# class ChebConv(nn.Module):
# def __init__(self,in_c,hid_c,out_c):
# super(ChebConv,self).__init__()
#
# self.ChebConv1 = pyg_nn.ChebConv(in_channels=in_c,out_channels=hid_c,K=1)
# self.ChebConv2 = pyg_nn.ChebConv(in_channels=hid_c,out_channels=out_c,K=1)
#
# def forward(self,data):
# x = data.x
# edge_index = data.edge_index
# hid = self.ChebConv1(x=x,edge_index=edge_index)
# hid = F.relu(hid)
#
# out = self.ChebConv2(hid,edge_index=edge_index)
# out = F.log_softmax(out,dim=1)
#
# return out
""" 2-GCN """
# class GraphCNN(nn.Module):
# def __init__(self, in_c,hid_c,out_c):
# super(GraphCNN,self).__init__()
#
# self.conv1 = pyg_nn.GCNConv(in_channels=in_c,out_channels=hid_c)
# self.conv2 = pyg_nn.GCNConv(in_channels=hid_c,out_channels=out_c)
#
# def forward(self,data):
# #data.x,data.edge_index
# x = data.x # [N,C]
# edge_index = data.edge_index # [2,E]
# hid = self.conv1(x=x,edge_index=edge_index) #[N,D]
# hid = F.relu(hid)
#
# out = self.conv2(hid,edge_index=edge_index) # [N,out_c]
#
# out = F.log_softmax(out,dim=1)
#
# return out
def main():
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
Cora_dataset = get_data()
#my_net = GATConv(in_c=Cora_dataset.num_node_features, hid_c=100, out_c=Cora_dataset.num_classes)
#my_net = SAGEConv(in_c=Cora_dataset.num_node_features, hid_c=40, out_c=Cora_dataset.num_classes)
my_net = SGConv(in_c=Cora_dataset.num_node_features,hid_c=100,out_c=Cora_dataset.num_classes)
#my_net = ChebConv(in_c=Cora_dataset.num_node_features,hid_c=20,out_c=Cora_dataset.num_classes)
# my_net = GraphCNN(in_c=Cora_dataset.num_node_features,hid_c=12,out_c=Cora_dataset.num_classes)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
my_net = my_net.to(device)
data = Cora_dataset[0].to(device)
optimizer = torch.optim.Adam(my_net.parameters(),lr=1e-3)
#model train
my_net.train()
for epoch in range(500):
optimizer.zero_grad()
output = my_net(data)
loss = F.nll_loss(output[data.train_mask],data.y[data.train_mask])
loss.backward()
optimizer.step()
print("Epoch",epoch+1,"Loss",loss.item())
#model test
my_net.eval()
_,prediction = my_net(data).max(dim=1)
target = data.y
test_correct = prediction[data.test_mask].eq(target[data.test_mask]).sum().item()
test_number = data.test_mask.sum().item()
print("Accuracy of Test Sample:",test_correct/test_number)
if __name__ == '__main__':
main()
Cora()
Epoch 1 Loss 4.600048542022705
Epoch 2 Loss 4.569146156311035
Epoch 3 Loss 4.535804271697998
Epoch 4 Loss 4.498434543609619
Epoch 5 Loss 4.456351280212402
Epoch 6 Loss 4.409425258636475
Epoch 7 Loss 4.357522964477539
Epoch 8 Loss 4.3007612228393555
Epoch 9 Loss 4.2392096519470215
Epoch 10 Loss 4.172731876373291
Epoch 11 Loss 4.101400375366211
Epoch 12 Loss 4.025243282318115
...............
Epoch 494 Loss 0.004426263272762299
Epoch 495 Loss 0.004407935775816441
Epoch 496 Loss 0.004389731213450432
Epoch 497 Loss 0.004371633753180504
Epoch 498 Loss 0.004353662021458149
Epoch 499 Loss 0.0043357922695577145
Epoch 500 Loss 0.004318032879382372
Accuracy of Test Sample: 0.794
边栏推荐
- When using lambda to pass parameters in a loop, the parameters are always the same value
- C语言读取BMP文件
- 天梯赛练习集题解LV1(all)
- Some concepts often asked in database interview
- Linux Yum install MySQL
- [MRCTF2020]套娃
- L2-004 is this a binary search tree? (25 points)
- Double to int precision loss
- Basic use of pytest
- Mysql的索引实现之B树和B+树
猜你喜欢
Case analysis of data inconsistency caused by Pt OSC table change
Redis面试题
Mall project -- day09 -- order module
Mtcnn face detection
【flink】flink学习
MySQL realizes read-write separation
Implementation scheme of distributed transaction
快来走进JVM吧
Password free login of distributed nodes
Kept VRRP script, preemptive delay, VIP unicast details
随机推荐
Dependency in dependencymanagement cannot be downloaded and red is reported
Word排版(小计)
分布式事务的实现方案
L2-004 is this a binary search tree? (25 points)
jS数组+数组方法重构
Kaggle竞赛-Two Sigma Connect: Rental Listing Inquiries(XGBoost)
express框架详解
Word typesetting (subtotal)
About string immutability
[NPUCTF2020]ReadlezPHP
[template] KMP string matching
[Blue Bridge Cup 2017 preliminary] buns make up
Mtcnn face detection
SQL time injection
【Flink】CDH/CDP Flink on Yarn 日志配置
Contiki源码+原理+功能+编程+移植+驱动+网络(转)
互聯網協議詳解
电商数据分析--用户行为分析
树莓派 轻触开关 按键使用
Kept VRRP script, preemptive delay, VIP unicast details